Automated decisioning systems have been developed to aid people and businesses to make faster, fact-based decisions in business settings. Typically, automated decisioning systems enable the user to make real-time, informed decisions, while minimizing risk and increasing profitability. Decisioning systems can be used to quickly assess risk potential, streamline account application processes, and apply decision criteria more consistently for approving decisions and/or selling new products or services.
Conventionally, decisioning systems have been based on predictive models and agents that do not adapt or learn from experience. Such predictive systems remain static after creation and must be manually updated. Adaptive agents and models have been developed which automatically learn from past experience. Examples of adaptive models used in the context of decisioning are described in co-pending U.S. patent application Ser. No. 11/963,501 entitled “Variable Learning Rate Automated Decisioning” filed on Dec. 21, 2007. However, the techniques described therein do not provide systems for setting up, maintaining and running systems for managing the adaptive agents and models.